Neural networks for the prediction of organic chemistry reactions

ACS Central Science

Volumn 2, Issue 10, 2016, Pages 725-732

  Wei, Jennifer N ^a   Duvenaud, David ^b   Aspuru Guzik, Alán ^a  

^a HARVARD UNIVERSITY   (United States)

^b HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EXPOSED TO; FINGERPRINTING METHODS; ORGANIC CHEMISTRY; REACTION PREDICTION; REACTION TYPES;

FORECASTING;

EID: 85012967324     PISSN: 23747943     EISSN: 23747951     Source Type: Journal    
DOI: 10.1021/acscentsci.6b00219     Document Type: Article

References (53)

1. Todd, M. H. Computer-aided organic synthesis. Chem. Soc. Rev. 2005, 34, 247.
2. Szymkuć, S.; Gajewska, E. P.; Klucznik, T.; Molga, K.; Dittwald, P.; Startek, M.; Bajczyk, M.; Grzybowski, B. A. Computer-Assisted Synthetic Planning: The End of the Beginning. Angew. Chem., Int. Ed. 2016, 55, 5904-5937.
3. Corey, E. J. Centenary lecture. Computer-assisted analysis of complex synthetic problems. Q. Rev., Chem. Soc. 1971, 25, 455-482.
4. Corey, E.; Wipke, W. T.; Cramer, R. D., III; Howe, W. J. Techniques for perception by a computer of synthetically significant structural features in complex molecules. J. Am. Chem. Soc. 1972, 94, 431-439.
5. Corey, E.; Howe, W. J.; Orf, H.; Pensak, D. A.; Petersson, G. General methods of synthetic analysis. Strategic bond disconnections for bridged polycyclic structures. J. Am. Chem. Soc. 1975, 97, 6116-6124.
6. Corey, E.; Long, A. K.; Greene, T. W.; Miller, J. W. Computerassisted synthetic analysis. Selection of protective groups for multistep organic syntheses. J. Org. Chem. 1985, 50, 1920-1927.
7. Corey, E.; Wipke, W. T.; Cramer, R. D., III; Howe, W. J. Computer-assisted synthetic analysis. Facile man-machine communication of chemical structure by interactive computer graphics. J. Am. Chem. Soc. 1972, 94, 421-430.
8. Jorgensen, W. L.; Laird, E. R.; Gushurst, A. J.; Fleischer, J. M.; Gothe, S. A.; Helson, H. E.; Paderes, G. D.; Sinclair, S. CAMEO: a program for the logical prediction of the products of organic reactions. Pure Appl. Chem. 1990, 62, 1921-1932.
9. Gasteiger, J.; Hutchings, M. G.; Christoph, B.; Gann, L.; Hiller, C.; Löw, P.; Marsili, M.; Saller, H.; Yuki, K. Organic Synthesis, Reactions and Mechanisms; Springer: Berlin, Heidelberg, 1987; pp 19-73.
10. Satoh, H.; Funatsu, K. Further Development of a Reaction Generator in the SOPHIA System for Organic Reaction Prediction. Knowledge-Guided Addition of Suitable Atoms and/or Atomic Groups to Product Skeleton. J. Chem. Inf. Comput. Sci. 1996, 36, 173-184.
11. Satoh, K.; Funatsu, K. A Novel Approach to Retrosynthetic Analysis Using Knowledge Bases Derived from Reaction Databases. J. Chem. Inf. Comput. Sci. 1999, 39, 316-325.
12. ChemPlanner. http://www.chemplanner.com/.
13. Gelernter, H.; Rose, J. R.; Chen, C. Building and refining a knowledge base for synthetic organic chemistry via the methodology of inductive and deductive machine learning. J. Chem. Inf. Model. 1990, 30, 492-504.
14. Chen, J. H.; Baldi, P. No Electron Left Behind: A Rule-Based Expert System To Predict Chemical Reactions and Reaction Mechanisms. J. Chem. Inf. Model. 2009, 49, 2034-2043.
15. Chen, J. H.; Baldi, P. Synthesis Explorer: A Chemical Reaction Tutorial System for Organic Synthesis Design and Mechanism Prediction. J. Chem. Educ. 2008, 85, 1699.
16. Law, J.; Zsoldos, Z.; Simon, A.; Reid, D.; Liu, Y.; Khew, S. Y.; Johnson, A. P.; Major, S.; Wade, R. A.; Ando, H. Y. Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation. J. Chem. Inf. Model. 2009, 49, 593-602.
17. Gothard, C. M.; Soh, S.; Gothard, N. A.; Kowalczyk, B.; Wei, Y.; Baytekin, B.; Grzybowski, B. A. Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One-Pot Reactions in the Network of Organic Chemistry. Angew. Chem. 2012, 124, 8046-8051.
18. Grzybowski, B. A.; Bishop, K. J. M.; Kowalczyk, B.; Wilmer, C. E. The 'wired' universe of organic chemistry. Nat. Chem. 2009, 1, 31-36.
19. Zimmerman, P. M. Automated discovery of chemically reasonable elementary reaction steps. J. Comput. Chem. 2013, 34, 1385-1392.
20. Wang, L.-P.; Titov, A.; McGibbon, R.; Liu, F.; Pande, V. S.; Martínez, T. J. Discovering chemistry with an ab initio nanoreactor. Nat. Chem. 2014, 6, 1044-1048.
21. Wang, L.-P.; McGibbon, R. T.; Pande, V. S.; Martinez, T. J. Automated Discovery and Refinement of Reactive Molecular Dynamics Pathways. J. Chem. Theory Comput. 2016, 12, 638-649.
22. Xu, L.; Doubleday, C. E.; Houk, K. Dynamics of 1, 3-Dipolar Cycloaddition Reactions of Diazonium Betaines to Acetylene and Ethylene: Bending Vibrations Facilitate Reaction. Angew. Chem. 2009, 121, 2784-2786.
23. Rappoport, D.; Galvin, C. J.; Zubarev, D. Y.; Aspuru-Guzik, A. Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry. J. Chem. Theory Comput. 2014, 10, 897-907.
24. Socorro, I. M.; Taylor, K.; Goodman, J. M. ROBIA: A Reaction Prediction Program. Org. Lett. 2005, 7, 3541-3544.
25. Socorro, I. M.; Taylor, K.; Goodman, J. M. ROBIA: a reaction prediction program. Org. Lett. 2005, 7, 3541-3544.
26. He, K.; Zhang, X.; Ren, S.; Sun, J. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE Int. Conf. Comput. Vision (ICCV) 2015, 1026-1034.
27. Krizhevsky, A.; Sutskever, I.; Hinton, G. E. In Adv. Neural. Inf. Process. Syst. 25;
28. Pereira, F., Burges, C. J. C., Bottou, L., Weinberger, K. Q., Eds.; Curran Associates, Inc.: 2012; pp 1097-1105.
29. Mnih, V.; et al. Human-level control through deep reinforcement learning. Nature 2015, 518, 529-533.
30. Silver, D.; et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016, 529, 484-489.
31. Reymond, J.-L.; Van Deursen, R.; Blum, L. C.; Ruddigkeit, L. Chemical space as a source for new drugs. MedChemComm 2010, 1, 30-38.
32. Kayala, M. A.; Baldi, P. ReactionPredictor: Prediction of Complex Chemical Reactions at the Mechanistic Level Using Machine Learning. J. Chem. Inf. Model. 2012, 52, 2526-2540.
33. Kayala, M. A.; Azencott, C.-A.; Chen, J. H.; Baldi, P. Learning to Predict Chemical Reactions. J. Chem. Inf. Model. 2011, 51, 2209-2222.
34. Morgan, H. L. The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. J. Chem. Doc. 1965, 5, 107-113.
35. Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. 2010, 50, 742-754.
36. Pyzer-Knapp, E. O.; Li, K.; Aspuru-Guzik, A. Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery. Adv. Funct. Mater. 2015, 25, 6495-6502.
37. Ballester, P. J.; Mitchell, J. B. O. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 2010, 26, 1169-1175.
38. Zhang, S.; Golbraikh, A.; Oloff, S.; Kohn, H.; Tropsha, A. A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models. J. Chem. Inf. Model. 2006, 46, 1984-1995.
39. Podolyan, Y.; Walters, M. A.; Karypis, G. Assessing Synthetic Accessibility of Chemical Compounds Using Machine Learning Methods. J. Chem. Inf. Model. 2010, 50, 979-991.
40. Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarelli, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. Convolutional Networks on Graphs for Learning Molecular Fingerprints. NIPS'15 Proc. 28th Int. Conf. Neural Inf. Process. Syst. 2015, 2224-2232.
41. Montavon, G.; Hansen, K.; Fazli, S.; Rupp, M.; Biegler, F.; Ziehe, A.; Tkatchenko, A.; Lilienfeld, A. V.; Müller, K.-R. Learning invariant representations of molecules for atomization energy prediction. Adv. Neural Inf. Process. Syst. 2012, 440-448.
42. von Lilienfeld, O. A.; Ramakrishnan, R.; Rupp, M.; Knoll, A. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties. Int. J. Quantum Chem. 2015, 115, 1084-1093.
43. Carhart, R. E.; Smith, D. H.; Venkataraghavan, R. Atom pairs as molecular features in structure-activity studies: definition and applications. J. Chem. Inf. Model. 1985, 25, 64-73.
44. Schneider, N.; Lowe, D. M.; Sayle, R. A.; Landrum, G. A. Development of a Novel Fingerprint for Chemical Reactions and Its Application to Large-Scale Reaction Classification and Similarity. J. Chem. Inf. Model. 2015, 55, 39-53.
45. Virshup, A. M.; Contreras-García, J.; Wipf, P.; Yang, W.; Beratan, D. N. Stochastic Voyages into Uncharted Chemical Space Produce a Representative Library of All Possible Drug-Like Compounds. J. Am. Chem. Soc. 2013, 135, 7296-7303.
46. Mann, M.; Ekker, H.; Flamm, C. The graph grammar library'a generic framework for chemical graph rewrite systems. In International Conference on Theory and Practice of Model Transformations; 2013; pp 52-53.
47. Schurer, S. C.; Tyagi, P.; Muskal, S. M. Prospective Exploration of Synthetically Feasible, Medicinally Relevant Chemical Space. J. Chem. Inf. Model. 2005, 45, 239-248.
48. Gothard, C. M.; Soh, S.; Gothard, N. A.; Kowalczyk, B.; Wei, Y.; Baytekin, B.; Grzybowski, B. A. Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One-Pot Reactions in the Network of Organic Chemistry. Angew. Chem. 2012, 124, 8046-8051.
49. Wade, L. G. Organic chemistry, 6th ed.; Pearson: Upper Saddle River, NJ, USA, 2013; p 377.
50. Bajusz, D.; Rácz, A.; Héberger, K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminf. 2015, 7, 1-13.
51. RDKit: Open-source cheminformatics; http://www.rdkit.org.
52. Bergstra, J.; Komer, B.; Eliasmith, C.; Yamins, D.; Cox, D. D. Hyperopt: a Python library for model selection and hyperparameter optimization. Comput. Sci. Discovery 2015, 8, 014008.
53. Pedregosa, F.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825-2830.